A lot of firms now are prioritizing the most important element collection and accuracy. This is why investing to outsource data entry best practices to companies like Magellan Solutions is the current business trend.
With accurate data, clients and agents could gain insights as well. This is the reason organizations consider data quality over data quantity. Too much focus only on collecting data without thinking about its quality will not help them succeed in today’s data-driven markets.
Companies with data as an asset can improve their everyday decision-making. If decisions are made with poor data, insights will be lacking. The decisions made will impact will be disastrous.
Furthermore, inaccuracies resulting from poor data quality are a barrier to a leap towards digital transformation.
8 out of 10 Machine learning (ML) and artificial intelligence (AI) are withheld. This is because of dirty or low-quality data. 96% of these projects have run into data quality problems and data labeling required to train AI.
Data entry done without appropriate skills and adequate technological tools leads to errors. These errors may seem small but can cost organizations millions of dollars, time, and efficiency to correct. Below are the bad, and how we make them into data entry best practices:
Top 10 of Magellan Solutions:
Correcting errors with data entry best practices
|Best Practices||Bad Practices||Transforming Bad To Best|
|1||Ensure data standards are in place.
All data entry projects must have a minimal set of standards for the operators to comply with. These should be compatible with the kind of data entry project.
Moreso, the data entry system used and the input sources of data should also be considered.
|Ignoring the purpose of the data.||Thoroughly knowing the purpose of the data system you create leads to considerations in the choice of the database engine, the entities to design, the record size and format, and the database engine management policies.
Ignoring these will lead to flawed designs although structurally and mathematically correct.
|2||Ensure that you correct the entered data, post data entry.
Spreadsheet tools provide data validation tools. It allows the user to control the kind of information to enter. Using these can provide users with choices and restrict specific entries.
In this regard, data entry form can ensure more accurate data entry than entering data in a spreadsheet. Through this, you enforce data entry rules at the time of data entry. The filled data can then be inserted into a spreadsheet.
Excel also has a comparable data validation tool in the form of dropdown lists. It helps enforce data entry rules.
In comparison, relational databases offer a powerful way of entering and storing data. Especially the complex and high volumes of data. They too have data validation tools that ensure the enforcement of data entry rules.
|Poor Normalization.||We are often faced with databases that were designed on the fly without following the most basic rules of normalization. We have to be clear. Every database should, at least, be normalized to a normal form. It is the layout that best represents your organizing projects. It symbolizes what treatment you offer for partners and customers.
If you stumble with tables that do not comply with 3NF, 2NF, or even 1NF, consider redesigning these tables.
|3||Post Data Entry Storage
Once you carry out the data entry task, ensure that it is saved in a format that can be read by any application. Avoid using a proprietary output format. The data would be lost when that format becomes obsolete. Good non-proprietary formats include ASCII, Unicode. They are open, unencrypted, and uncompressed.
|Redundancy||This is an overhead that can be avoided if normalization rules are followed thoroughly. Although sometimes redundancy may seem necessary. But it must be used only in very specific cases and be clearly documented in order to be taken into consideration in future developments.
• Unnecessary increase of database size
• Data being prone to inconsistency
• Decreases in the efficiency
• Data corruption
|4||Familiarization with poor data entry practices.
Data entry operators should be trained in recognizing and identifying data entry errors. Some of these errors are often found in excel files. It is because different personnel enter data at different times. These errors include:
• Inconsistent formats for name, location, contact fields results in confusion and difficulties in making sense of the data
• Wrong order of columns
• Inserting different types of information in one column
Making use of efficient data cleansing strategies can help.
|Bad Referential Integrity (Constraints)||If no constraints or very few constraints are implemented from the design stage, the data integrity will have to rely entirely on the business logic. It results in human errors.|
One very good practice in Excel-based data entry is to create descriptive names for files and columns. One that does not have spaces or special characters. The latter may create problems when the data file is used for subsequent analysis.
The descriptor may include details such as source, date, version, project, etc.
|Not taking advantage of DB Engine (DBE) features.||Not knowing or ignoring DBE capabilities will take development to an extremely uncertain path and surely to bugs and future problems.|
|6||Consistency in column & row filling.
Ensure that data is entered consistently in the same way in a single datasheet.
Avoid chunks or blocks located in different places. You can do this by tagging different columns with different labels. These labels indicate the alphabet or numeric status and consistently fill them with numbers or letters.
It becomes easier to understand that data and work on it.
|Composite Primary Keys||If your table with a composite primary key has a million rows, the index controlling the composite key can grow to a point where CRUD operation performance is very degraded.
It is a lot better to use a simple integer ID primary key. Its index will be compact enough and establish the necessary DBE constraints to maintain uniqueness.
Most of the time datasets end up having missing data. These can lead to significant losses if they are not identified and located distinctly during data entry procedures. There are several ways of treating missing data:
• One way is to leave the field empty. You can assign a NULL value or NO value to it.
• Enter 9999, a distinct value, to indicate a missing number in a numeric field, if you cannot assign other values.
• In text fields, it is good practice to use NA or Not Available or Not Applicable in the missing data field.
• Data flags can be placed in a separate column to define the missing value.
|Poor indexing.||Indexing is always a delicate decision. Too much indexing can be as bad as too little.
If the table is big enough, you create an index on each column. With this, the performance of SELECTs improves. But INSERTs, UPDATEs, and DELETEs drop.
Indexes have to be kept synchronized with the table.
On the other hand, having a table with no index on columns will lead to poor performance on SELECTs.
Also, index efficiency depends sometimes on the column type. INT columns show the best possible performance. But indexes on VARCHAR, DATE or DECIMAL are not as efficient. This consideration can even lead to redesigning tables that need to be accessed with the best possible efficiency.
|8||Complete lines of data.
Spreadsheets may be powerful tools. But not entering data properly in a spreadsheet leads to issues.
During sorting, columns get sorted independently. Therefore, it is good practice to ensure that all cells in a single line are filled completely.
|Poor Naming Conventions||The table name must describe what entity it holds. Each column name must describe what piece of information it represents.
It starts to be complicated when tables have to relate to each other. Names start to become messy. An even worse scenario is if there are confusing naming conventions with illogical norms like “column name must be 8 characters or less”.
|9||Keeping a log.
It provides a record of errors and difficulties that you encountered when carrying data entry out. Each project would require an entry log. The log records:
• Number of fields from which information is missing
• Wrong and inaccurate data is entered
• Fields that need clarification
• When was the error noted
• When was the action taken
It provides a systematic account of the process efficiency. It is useful in fine-tuning data cleansing and project management.
The data entry project manager has to take responsibility for ensuring the completeness and accuracy of the log. The log can be useful in tracing back errors detected later.
|Ambiguous data||Data entry errors due to ambiguity are usually due to overlooking small details. Suppose a company follows the European date format of DD/MM/YY. In this case, it is easy for whoever is making the entries to use the MM/DD/YY format. Company formats and codes should guide data entry.|
When necessary, use automation to carry out large volumes of data entry but remember that manual validation would need to be done in order to ensure accuracy.
|Change-induced inconsistencies.||The errors due to value representation consistency are not always easily detected. The said “incorrect” value might be technically correct.
In a situation where the word of choice in a field is “free-form,” and the term used is “free form,” they look the same but mean different things. Also, in a case where an integer replaces decimals, for example, “1” replaces “1.0”. They have the same value but could mean different things.
Every value, word, and figure used must be consistent all through the data documented.
Data losses are due to human errors. But they can be easily avoided if you follow the tips that this article covers.
Data entry mistakes are not limited to what is listed here.
To improve data entry accuracy, data entry service providers should make use of more automated systems to help with data entry and less manual work. This would help to reduce errors and improve accuracy. Also, the data entry processes utilized should be streamlined. This would make it easier for the accuracy of data to be monitored and thereby reduce errors.
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